Table 4 Fitness performance: the proposed algorithm comparing with evolutionary algorithms on smart IoT application.

1

20

20

0.169949

0.144572

0.168411

0.136882

0.171487

0.146879

0.174563

0.149955

0.176101

0.151493

2

40

20

0.210834

0.179352

0.208926

0.169812

0.212742

0.182214

0.216558

0.18603

0.218466

0.187938

3

60

20

0.251719

0.214132

0.249441

0.202742

0.253997

0.217549

0.258553

0.222105

0.260831

0.224383

4

80

20

0.292604

0.248912

0.289956

0.235672

0.295252

0.252884

0.300548

0.25818

0.303196

0.260828

5

100

20

0.333489

0.283692

0.330471

0.268602

0.336507

0.288219

0.342543

0.294255

0.345561

0.297273

6

120

20

0.374374

0.318472

0.370986

0.301532

0.377762

0.323554

0.384538

0.33033

0.387926

0.333718

7

140

20

0.415259

0.353252

0.411501

0.334462

0.419017

0.358889

0.426533

0.366405

0.430291

0.370163

8

160

20

0.456144

0.388032

0.452016

0.367392

0.460272

0.394224

0.468528

0.40248

0.472656

0.406608

9

180

20

0.497029

0.422812

0.492531

0.400322

0.501527

0.429559

0.510523

0.438555

0.515021

0.443053

10

200

20

0.537914

0.457592

0.533046

0.433252

0.542782

0.464894

0.552518

0.47463

0.557386

0.479498

11

220

20

0.578799

0.492372

0.573561

0.466182

0.584037

0.500229

0.594513

0.510705

0.599751

0.515943

12

240

20

0.619684

0.527152

0.614076

0.499112

0.625292

0.535564

0.636508

0.54678

0.642116

0.552388

13

260

20

0.660569

0.561932

0.654591

0.532042

0.666547

0.570899

0.678503

0.582855

0.684481

0.588833

14

280

20

0.701454

0.596712

0.695106

0.564972

0.707802

0.606234

0.720498

0.61893

0.726846

0.625278

15

300

20

0.742339

0.631492

0.735621

0.597902

0.749057

0.641569

0.762493

0.655005

0.769211

0.661723

16

320

20

0.783224

0.666272

0.776136

0.630832

0.790312

0.676904

0.804488

0.69108

0.811576

0.698168

17

340

20

0.824109

0.701052

0.816651

0.663762

0.831567

0.712239

0.846483

0.727155

0.853941

0.734613

18

360

20

0.864994

0.735832

0.857166

0.696692

0.872822

0.747574

0.888478

0.76323

0.896306

0.771058

19

380

20

0.883779

0.751812

0.875781

0.711822

0.891777

0.763809

0.907773

0.779805

0.915771

0.787803

20

400

20

0.902564

0.767792

0.894396

0.726952

0.910732

0.780044

0.927068

0.79638

0.935236

0.804548

21

420

20

0.921349

0.783772

0.913011

0.742082

0.929687

0.796279

0.946363

0.812955

0.954701

0.821293

22

440

20

0.940134

0.799752

0.931626

0.757212

0.948642

0.812514

0.965658

0.82953

0.974166

0.838038

23

460

20

0.940134

0.799752

0.931626

0.757212

0.948642

0.812514

0.965658

0.82953

0.974166

0.838038

24

480

20

0.940355

0.79994

0.931845

0.75739

0.948865

0.812705

0.965885

0.829725

0.974395

0.838235

25

500

20

0.940576

0.800128

0.932064

0.757568

0.949088

0.812896

0.966112

0.82992

0.974624

0.838432